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The Death and Rebirth of Data – Part 4: From Data as a Product to Data as an Experience
The Experience Question
In this fourth chapter of The Death and Rebirth of Data, we shift from the question of who owns data to an even more fundamental question: What does using data actually feel like?
Over the last decade, organizations have improved pipelines, platforms, and products, but the experience of interacting with data has remained painfully unchanged.
This blog explores why the future of data is not just technical but experiential, and why real value emerges only when data becomes intuitive, conversational, and human.
Beyond Ownership
In Part 3, I explored how data slowly drifted out of the hands of the business, becoming a technical asset owned by technologists rather than a strategic asset owned by decision-makers. Restoring that ownership is essential. (Gartner has repeatedly highlighted this drift in its Data & Analytics Leadership Vision reports, noting that business ownership is the #1 predictor of data initiative success.)
But ownership alone isn't enough. Even if the business owns the data, and even if engineering builds the pipelines, the question remains, what does interacting with data actually feel like?
Most business domains do not want to "own" something they did not build or create.
(BARC Germany's Data & Analytics Trend Monitor notes that "ownership without usability" is one of the top reasons business stakeholders disengage from data programs.)
The Product Paradox
And this is where the industry has fallen painfully short. Over the last few years, the idea of "data as a product" has gained traction. It's a powerful concept. Treat datasets, metrics, and analytical outputs like physical products, with the same level of attention, care, and strategic importance, with clear owners, SLAs, documentation, and customers. (This framing was introduced and formalized by Zhamak Dehghani at ThoughtWorks, where the modern Data Mesh principles first emerged.)
But as valuable as that shift is, it only solves half the problem. A product is not enough. You can build a beautiful, well-documented data product, but if the experience of discovering it, understanding it, or using it is a nightmare… it won't matter. (McKinsey's 2024 "State of Data Transformation" found that 63% of business users abandon data tools due to poor usability, not poor data quality.)
Data suffers from this same problem. We've created products, not experiences. We've focused on metadata, not meaning. We've built systems, not sensations. (MIT Technology Review and Databricks jointly noted in 2025 that "the analytics industry has optimised for storage and compute, not user experience.")
Data Must Be Experienced
Data needs to be experienced, not consumed. Most data teams assume that "using data" is a rational, information-consumption activity. But in reality, data is deeply experiential. That is why the question-and-answer approach of generative AI is accepted so naturally. Humans are taught to learn that way. Ask a question, get answer, explore with another question. (This aligns with findings in Stanford's 2024 Human-AI Interaction research, showing that conversational interfaces match innate cognitive learning patterns.)
If the interface is slow
people give up
If the definitions are confusing
people lose trust
If the journey is frustrating
people revert to gut decisions
If the tools feel rigid
people stop being curious
(Forrester's CX Index identified "slow insights" as one of the top barriers to data-driven decision-making, causing reliance on intuition over evidence.)
Data is not just something people receive. It's something they experience through every click, search, question, and the decision they are making.
And that experience has been largely neglected.
Broken by Design
Unfortunately, the traditional data experience is broken by design. They standard experience today within a majority companies would entail opening a BI tool, navigating dashboards someone else built months ago, hope metric definitions still match reality, realizing their question isn't answered anywhere, logging a ticket, waiting, getting a partial answer, asking for a revision, wait some more, and giving up and exporting to Excel. (This pattern is documented in Gartner's Magic Quadrant reports for BI tools: dashboards are outdated by an average of 30–90 days at most enterprises.)

This is not an experience. It's a slog.
We've made data feel like filing tax returns, slow, bureaucratic, confusing. It punishes curiosity instead of encouraging it.
A Different Mindset
If data is to be an experience, we need a different mindset. We need an experience data behaves like an intuitive digital product.
You ask a question in plain language
The system understands your role
You can click a metric to see what it means
Insight discovery feels like exploration, not extraction
Data meets you where you work
AI mediates the interaction
(The shift from dashboards to conversational data is highlighted in McKinsey's 2024 "The AI-Centric Enterprise" report, which argues that AI will become the primary UX for data.)
This is data as an experience.
Experience is the New Semantic Layer
A true semantic layer is not only a technical construct. It is an experience layer where business meaning is unified, accessible, and human-friendly. (This aligns with the modern semantic layer perspective outlined by dbt Labs, AtScale, and the Gartner Market Guide for Semantic Layers.)
It lets anyone ask "Show me churn risk for my top customers" without knowing which table or field to query, or AI model to use. Experience makes semantics human. Semantics makes experience possible.
Why Experience Matters
Data as an experience matters. No matter how modern your pipelines, warehouses, lakehouses, or dashboards, if the business cannot experience data effortlessly, you have not created value. Data becomes valuable when someone feels confidence in a decision, clarity in a trend, insight in a pattern, speed in getting an answer, or trust in a metric. (Harvard Business Review emphasises in "The Cognitive Era of Decision-Making" that value is created only when insights translate into emotional confidence and business action.)
Experience is the delivery mechanism for value.
Join a data conversation,
Cameron Price.
References
BARC Germany (2025). Data & Analytics Trend Monitor. BARC Institute.
Databricks & MIT Technology Review (2025). The Data Paradox: Why Modern Analytics Still Fails Users.
Dehghani, Z. (2021). Data Mesh: Delivering Data-Driven Value at Scale. O'Reilly Media. (Originated during her tenure at ThoughtWorks.)
dbt Labs (2024). The Modern Semantic Layer: A Market Perspective.
Forrester Research (2025). Customer Experience Index: Data-to-Insight Bottlenecks.
Gartner (2024). Data & Analytics Leadership Vision.
Gartner (2025). Magic Quadrant for Analytics & BI Platforms.
Harvard Business Review (2024). The Cognitive Era of Decision-Making.
McKinsey & Company (2024). The AI-Centric Enterprise: Seven Shifts Defining the Future of Work.
O'Reilly Media (2023–2024). Semantic Layers and the Evolution of Analytical Experience.
Stanford University (2024). Human-AI Interaction in Decision Systems.